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The implementat
aDepartment of Chemical and Environmenta
43400 Selangor, Malaysia. E-mail: soroush.bDepartment of Mechanical Engineering
Agricultural Engineering and Technology
Resources, University of Tehran, Karaj, IrancDepartment of Physics, Faculty of Science, IdInstitute of Advanced Technology, Unive
MalaysiaeDepartment of Chemistry, College of Scien
Saudi ArabiafLaboratoire de Recherche LR18ES08, Chemi
Manar University, Tunis 2092, TunisiagDepartment of Chemical Engineering, Facu
W. Persekutuan Kuala Lumpur, 50603 Kual
Cite this: RSC Adv., 2020, 10, 13302
Received 30th January 2020Accepted 26th February 2020
DOI: 10.1039/d0ra00892c
rsc.li/rsc-advances
13302 | RSC Adv., 2020, 10, 13302–1
ion of artificial neural networks forthe multivariable optimization of mesoporous NiOnanocrystalline: biodiesel application
Soroush Soltani, *a Taha Roodbar Shojaei,b Nasrin Khanian,c Thomas Shean YawChoong,a Umer Rashid,d Imededdine Arbi Nehdief and Rozita Binti Yusoffg
In the present research, artificial neural network (ANN) modelling was utilized to determine the relative
importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst.
Initially, carbonaceous nanocrystalline mesoporous NiO core–shell solid sphere composites were
produced by applying incomplete carbonized glucose (ICG) as the pore directing agent and polyethylene
glycol (PEG; 4000) as the surfactant via a hydrothermal-assisted method. The Brunauer–Emmett–Teller
(BET) model was applied to ascertain the textural characteristics of the as-prepared mesoporous NiO
catalyst. The effects of several key parameters such as metal ratio, surfactant and template
concentrations, and calcination temperature on the prediction of the surface areas of the as-synthesized
catalyst were evaluated. In order to verify the optimum hydrothermal fabrication conditions, ANN was
trained over five different algorithms (QP, BBP, IBP, LM, and GA). Among five different algorithms, LM-4-
7-1 representing 4 nodes in the input layer, 7 nodes in the hidden layer, and 1 node in the output layer
was verified as the optimum model due to its optimum numerical properties. According to the modelling
study, the calcination temperature demonstrated the most effective parameter, while the ICG
concentration indicated the least effect. By verifying the optimum hydrothermal fabrication conditions,
the thermal decomposition of ammonium sulphate (TDAS) was applied to the functionalized surface
areas and mesoporous walls by –SO3H functional groups. In addition, the catalytic performance and
reusability of the produced mesoporous SO3H–NiO catalyst were evaluated via the transesterification of
waste cooking palm oil, resulting in a methyl ester content of 97.4% and excellent stability for nine
consecutive transesterification reactions without additional treatments.
Introduction
The huge consumption of conventional diesel fuel has causedthe rapid exhaustion of petroleum resources. Besides, the threatof climate change and environmental pollution due to theemission of hazardous greenhouse gases is the one of the mostcritical issues across the world. Among alternative possible
l Engineering, Universiti Putra Malaysia,
of Agricultural Machinery, Faculty of
, College of Agriculture and Natural
slamic Azad University, Karaj, Iran
rsiti Putra Malaysia, 43400 Selangor,
ce, King Saud University, Riyadh 11451,
stry Department, Science College, Tunis El
lty of Engineering, University of Malaya,
a Lumpur, Malaysia
3315
sources, biodiesel has prompted intense interest as a capablesubstitute for current fuel.
The most favourable approaches of biodiesel production arethe conventional esterication of free fatty acids (FFAs) or thetransesterication of triglycerides (TG) along with alcohol overa proper base/acid catalyst. Recent advances in reusable solidacid catalysts are being paid great attention in many chemicalreactions such as esterication, transesterication, hydrolysis,and hydration. This class of catalysts, containing both Lewisand Brønsted types, has been proposed as an alternative to theun-reusable homogeneous acid/base catalysts. In contrast withhomogeneous base/acid catalysts, heterogeneous acid catalystspossess a number of advantages such as easy separation fromthe reaction medium, eradication of washing procedure, andminimization of corrosion. Hydrophobicity is another merit ofheterogeneous acid catalysts, which prevents the access of polarby-products to a majority of the active sites. It is noteworthy tomention that the distribution of polar reagents into the exterioractive surface of the catalyst may cause the de-activation of thecatalyst.1 In addition, these type of catalysts can be merelyrecovered and reused, which considerably diminish the rate of
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fabrication.2 As a result, the environment friendly heteroge-neous acid catalysts are likely to be swapped with ecologicallyunfavourable homogeneous acid catalysts.
Moreover, conventional heterogeneous acid catalysts havelost their position in inorganic syntheses because of their poorstructural, physicochemical, and textural properties that limitthe reach of the reagents to the active sites through catalyticreactions. In this case, synthesizing novel mesoporous catalystswith a high specic surface area (SBET) and exible and homo-geneous pore diameters (Dp) have attracted the most attentionin inorganic syntheses. In general, the fabrication of meso-structured materials can be done through templatingapproaches in the presence of a surfactant.3,4 Nevertheless,there are several key parameters such as metal ratio, surfactantand template concentrations, and annealing temperature thatinuence the SBET of the synthesized catalyst.5 However, it isquite difficult to estimate the relative importance of these crit-ical parameters on the specic surface areas of the synthesizedcatalyst. Therefore, well-known multi-variate semi-empiricalapproaches such as response surface methodology (RSM) andarticial neural networks (ANNs) can be applied to optimize thepossible relative condition.6–9
The RSM projects the related conditions, ts the detectedpractical outcomes of the executed model at that point, andthen proposes the most suitable model for further authentica-tion. In this method, the conrmedmodel is typically applied togain the maximum yield of the nal product. However, themodelling process involves some complex numerical calcula-tions such as tting and the reversion process. On the otherhand, the ANN modelling is free of any complications due tohigh reliability in obtaining non-linear association amongst theinput and output variables.10–12 Unlike RSM, the ANNmodellingis not funded on mathematical models and can be simplyapplied for simulation of such a complex system.13–16 ThroughANN modelling, the output is typically generated aer accu-mulation of input data via processing of the fundamentalelements. On the other hand, the output is identied uponlearning from a sequence of cases without knowing about therelation between them.17–19 However, the main challenge is toverify a suitable algorithm among various algorithms [quickpropagation (QP), Levenberg–Marquardt (LM), genetic algo-rithm (GA), incremental back-propagation (IBP), and batchback-propagation (BBP)] through the learning procedure.20
Through the process, the weights and biases should improve tosimultaneously lessen the inaccuracies and enhance the capa-bility of the method. Subsequently, the miscalculation value isdetermined between the predicted and real output data.21,22
Furthermore, ANN as a non-linear statistical analysis tech-nique has been vastly applied in various nanotechnologyapplications.6,23–27 Generally, it is considered as a promisingsimulation technique, which hugely diminish the rate offabrication by predicting the output results with a short level oferror in the prediction.
In this research work, the carbonaceous mesoporous NiOcore–shell solid sphere composites were produced usingincomplete carbonized glucose (ICG) as the pore directing agentand polyethylene glycol (PEG; 4000) as the surfactant via
This journal is © The Royal Society of Chemistry 2020
hydrothermal assisted method. The effect of different hydro-thermal reactions such as metal ratio, surfactant and templateconcentrations, and calcination temperature on the predictionof the surface areas of the synthesized catalyst were modelled byANN. Aerwards, the thermal decomposition of ammoniumsulphate (TDAS) was applied to enrich the surface areas andmesopore walls with –SO3H functional groups. It is known thatintroducing hydrophobic organic species via post-syntheticapproaches results in severe lessening of the textural proper-ties. The reduction of porosity and surface area could beassigned to the growth of functional species into the mesoporechannels as the exterior and interior surface areas were drasti-cally improved in the presence of hydrophobic species. Byhaving this knowledge, the effect of only the hydrothermalparameters was evaluated and post-sulfonation treatment wasonly applied to maximize the functionality of the catalystthrough transesterication reaction. Furthermore, the catalyticactivity and reusability of the produced mesoporous SO3H–NiO-ICG catalyst were determined via transesterication of wastecooking palm oil (WCPO).
Materials and methodsMaterials
The analytical chemicals polyethylene glycol [PEG, Mn ¼ 4000 gmol�1], nickel nitrate [Ni(NO3)2$6H2O], and methanol (CH3OH;$99.5%) were acquired from Fisher Scientic. D-Glucose(C6H12O6), ammonium sulphate [(NH4)2SO4;$99.5%], and urea[CO(NH2)2] were acquired from Sigma-Aldrich. The basic methylesters for gas chromatography (GC) analysis (heptadecanoate,methyl-myristate, methyl-stearate, methyl-linoleate, methyl-palmitate, and methyl-oleate) were delivered by Fluka, USA.
The WCPO [holding 17.0% FFAs, acid value of 34.0 mg KOHper g, saponication value of 171.0 mg KOH per g, density (15�C) of 0.91 g cm�3, and kinematic viscosity (40 �C) of 33.0 mm2
s�1] was obtained from a local market, Malaysia. The suppliedWCPO contained ve most important FFAs including myristicacid (C14:0, 2.70%), stearic acid (C18:0, 7.13%), linoleic acid(C18:2, 12.59%), palmitic acid (C16:0, 34.80%), and oleic acid(C18:1, 42.78%).
Synthesis of the mesoporous SO3H–NiO-ICG catalyst
Synthesis of mesoporous NiO-ICG catalyst. In this study,mesoporous NiO-ICG catalysts were synthesized hydrothermallyin the presence of PEG as the surfactant and ICG as thetemplate, as per our previous study.28 Initially, 10 g of D-glucosewas placed into a muffle furnace tube where the pyrolysisprocess took place in the presence of N2 gas ow (at the rate of100 mL min�1) at 400 �C for 12 h. The synthesized powder wasmilled at 1000 rpm for 1 h and pounded to develop a uniformlyne ICG powder.
The autoclave-assisted system was used to fabricate a poly-meric mesoporous NiO-ICG catalyst where nickel nitrate (2 g),PEG (10 g), urea (5 mmol), and ICG : Ni (5 : 1) were mixed into150mL deionized water under constant stirring. Next, the Teon-lined stainless-steel autoclave was taped up and sustained at
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200 �C for 18 h under autogenous-pressure. Aerwards, post-calcination at 600 �C for 4 h in the presence of N2 gas owtook part to get rid of any kind of outstanding components asa result of the initial decomposition of the materials.
Post-functionalization through TDAS. The as-synthesizedmaterial was further treated by TDAS to improve the hydro-phobic character of the catalyst by growing the aromatic groupsand lessening the aliphatic rings. Here, 2 g of the as-preparedNiO-ICG nanocrystalline was added to 50 mL of ammoniumsulphate and sonicated for 60 min. Aerwards, the blend wastransferred into an autoclave, which was taped up and sus-tained at 200 �C for half an hour under autogenous-pressure.The prepared product was rigorously cleaned with a blend ofdeionized water and ethyl alcohol to diminish the unreactedcomponents attached to the catalyst. Next, the product wasdried up in an electric vacuum oven at 100 �C for 24 h. Ulti-mately, post-annealing treatment took place at 600 �C for 4 h inthe presence of N2 gas ow to diminish the residual volatilesand to fully activate the mesoporous SO3H–NiO-ICG catalyst.
Catalyst characterization
The evaluation of the textural properties of the produced mes-oporous NiO-ICG nanocrystalline material was performed usingthe Brunauer–Emmett–Teller (BET; Thermo Finnigan appa-ratus) model. The specic surface areas (SBET) were determinedby adsorption–desorption isotherm method while the poredistribution diameters were evaluated by Barrett–Joyner–Halenda (BJH) method. Prior to analysis, the catalyst wentthrough pre-treatment to eliminate moisture and was degassedat 150 �C for 120 min over H2 ow, and later the catalyst wassubmerged into a gassy vessel of N2 with the temperature of�196 �C. At the end, the adsorption of N2 was computed at therelative pressure.
To determine the acidity of the active sites, ammoniatemperature-programmed desorption (NH3-TPD; Thermo Fin-nigan TPDRO 1100) was introduced. Initially, a certain amountof mesoporous catalyst (0.4 g) was rst treated with Ar gas at150 �C and later, it was subjected to a frequent ow of NH3 for60 min. Finally, the adsorption of NH3 was measured andcomputed along with heating the catalyst up to 900 �C witha thermal conductivity detector (TCD) while the rate of heatingsystem was 15�C min�1 in 50 mL min�1 He gas.
X-ray diffraction (XRD; 6000 Shimadzu) was utilized to assessthe physical and structural characteristics of the producednanocrystalline material.
The microscopic morphology of NiO-ICG was characterizedusing transmission electron microscopy (TEM, Hitachi H-7100)and eld emission scanning electron microscopy (FESEM, FEINovanano 240) tted with an energy dispersive X-ray (EDX)spectrometer (DMAX microscope at 200 kV).
The ANN modelling and learning procedure
In the process of ANN modelling, there are three majors layerincluding input, hidden, and output; each single layer containsseveral nodes that are typically linked by either multilayer feed-back or feed-forward connection equation. In this case, the
13304 | RSC Adv., 2020, 10, 13302–13315
nodes of certain layers can be correlated to the nodes of thesubsequent layers. In general, the characteristics of the bio-logical neural networks can be simulated by articial neurons(nodes). In this procedure, the data of input layer's nodes withspecial weight will be transformed to the hidden layer and thenthe output layer's nodes. The simulation is fullled via relatedweights throughout learning progression by well-knownlearning algorithms.29
During this study, a multilayer feed-forwards neural network(NN) was applied via ve well-known common learning systemsincluding quick propagation (QP), batch back propagation(BBP), incremental back propagation (IBP), Levenberg–Mar-quardt (LM), and genetic algorithm (GA).30,31 Series of reactionswere conducted to evaluate the impact of hydrothermal reactionfactors on the textural properties. The inputs are metal ratio,surfactant and template concentrations, and calcinationtemperature while the output is the specic surface area. Overthe synthetic procedure, one parameter was varying and theother three parameters were kept constant. All the tentative datautilized for ANN modelling using NeuralPower soware version2.5 and are reviewed in Table 1. The test, training, andauthentication outputs were selected randomly by using RSM.
Throughout the learning procedure, the hidden layer and theoutput of the jth in the output-layer is calculated through eqn(1):
yi ¼Xi
j¼1
xiwij þ bj (1)
whereas, yi is the input of the system to j node in the hiddenlayer, i is the nodes' numbers, b is the bias terms, xi is the outputof the ex-layer, and wij is the inuence of linking between the ith
node and jth node.However, the main challenge is to minimize the errors by
verifying the proper summation of interconnection weightsthrough the training process.32 It is highly essential to assess theprediction ability of the ANN simulation system. The rootmeans square error (RMSE) is known as a unique assessmentequation which is calculated by eqn (2):33
RMSE ¼ 1=n
Xni¼1
ðyi � ydiÞ2!1=2
(2)
where n is the sum of the points, yi is the anticipated amount,and ydi is the actual amount.
It should be noted that the learning process keeps repeatingto avoid casual connotation between the weights. The factor ofdetermination (R2) imitates the t rate for the numericalpattern, which is calculated via eqn (3):34
R2 ¼ 1�Xni¼1
ðyi � ydiÞ2,Xn
i¼1
ðydi � ymÞ2 (3)
where, ym is the average of the actual values.The absolute average deviation (AAD) is one more critical
factor that commonly introduced us to assess the errorsbetween actual and predicted values. The ADD is calculated byeqn (4):17
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Table 1 Experimental outputs (training, testing, and validation), actual, and predicated values of specific surface area of the synthesizednanocrystalline NiO-ICG composites
Run no. ICG con. PEG con. Ni con. (mmol)Calcinationtemp. (�C)
Actual surfacearea (m2 g�1)
Predicted surfacearea (m2 g�1)
Training data set1 10 6 2 500 295 294.812 15 6 2 500 307 306.913 10 18 2 500 315 315.834 15 18 2 500 327 326.415 5 24 2 500 315 314.916 10 24 2 500 325 324.927 20 24 2 500 332 332.048 10 6 4 500 320 319.889 20 6 4 500 324 323.8210 5 12 4 500 308 307.7711 15 12 4 500 326 327.5912 20 12 4 500 321 320.9413 10 18 4 500 326 325.7514 15 18 4 500 338 336.5915 5 24 4 500 328 327.8816 10 24 4 500 335 334.6117 20 24 4 500 341 340.9918 5 6 6 500 322 321.6319 15 6 6 500 341 341.7520 20 6 6 500 335 335.0521 5 18 6 500 327 328.0122 10 18 6 500 335 334.2523 20 18 6 500 342 342.5824 5 24 6 500 333 333.2525 5 6 8 500 331 331.3126 10 6 8 500 338 337.7527 20 6 8 500 343 342.328 5 18 8 500 344 343.129 15 18 8 500 358 359.230 20 18 8 500 353 352.8531 10 24 8 500 357 357.7732 15 24 8 500 369 367.7433 5 6 2 600 291 291.1234 10 6 4 600 329 328.4235 20 6 8 600 350 350.1436 10 12 4 600 324 323.7937 10 18 4 600 333 334.2238 15 18 6 600 355 353.6939 5 24 2 600 321 320.9440 15 24 6 600 357 357.4841 10 6 4 700 316 316.3542 15 6 6 700 337 337.04
Training data set43 10 12 4 700 311 310.2144 5 18 2 700 302 301.8945 15 18 6 700 338 338.246 20 18 8 700 347 348.447 15 24 6 700 341 340.9648 20 24 8 700 358 356.5249 15 6 6 800 333 332.7850 20 6 8 800 332 332.8351 10 12 4 800 307 307.4652 20 12 8 800 336 334.3653 10 18 4 800 315 314.7454 5 24 2 800 287 287.0655 20 24 8 800 350 350.95
Test data set56 15 24 2 500 336 284.3
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Table 1 (Contd. )
Run no. ICG con. PEG con. Ni con. (mmol)Calcinationtemp. (�C)
Actual surfacearea (m2 g�1)
Predicted surfacearea (m2 g�1)
57 15 6 4 500 330 306.3958 10 12 4 500 317 319.1359 5 18 4 500 319 337.1360 20 18 4 500 333 331.1761 15 24 4 500 344 315.9262 10 6 6 500 330 319.2963 5 12 6 500 315 329.4264 10 18 8 500 349 346.2365 5 24 8 500 350 329.3366 20 24 8 500 364 320.8967 15 6 6 600 348 347.4768 5 18 2 600 314 339.4969 20 18 8 600 360 350.0170 10 18 4 700 320 348.2371 5 24 2 700 297 351.572 10 6 4 800 310 364.6873 5 12 2 800 286 349.4774 5 18 2 800 297 318.0975 15 24 6 800 334 360.61
Validation data set76 5 6 2 500 280 284.377 5 18 2 500 308 306.3978 20 18 2 500 322 319.1379 15 18 6 500 346 347.4780 10 24 6 500 339 339.4981 15 6 8 500 347 350.0182 20 24 8 600 370 368.3683 20 6 8 700 338 336.32
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AAD ¼ 1
n
Xni¼1
|yi � ydi|=ydi (4)
It is known that the greatest NN model should possess theleast RMSE value and maximum AAD and R2 values.35
Fig. 1 The chosen RMSE versus node number of mean specificsurface area of the NiO-ICG catalyst network's hidden layer for BBP,QP, IBP, GA, and LM.
Transesterication procedure and characterization
Prior to the reaction, WCPO was pre-treated (at 120 �C for20 min and then ltered) to get rid of moisture and otherremainders. The autoclave-assisted transesterication ofWCPO was performed to evaluate the catalytic activity andrecyclability of SO3H–NiO-ICG over the optimized conditions:the mesoporous catalyst amount of 1 wt%, methanol toWCPO molar ratio of 9 : 1, operating temperature of 100 �Cand a mixing intensity of 450 rpm. By the end of every singlereaction, the blend was centrifuged, residual methanol wasvaporized, and later, the remaining mixture was located ina splitting funnel to separate glycerol from the producedester.
The content of produced fatty acid methyl ester (FAME) wasfurther determined by gas chromatography ame ionizationdetector (GC-FID; Agilent 7890A) whereas methyl heptadeca-noate was used as an internal standard and methyl-myristate,methyl-stearate, methyl-linoleate, methyl-palmitate, and
13306 | RSC Adv., 2020, 10, 13302–13315
methyl-oleate were employed as the orientation standards. Thecontent of produced methyl ester was calculated by the EN14103 standard method via eqn (5):
C ¼PA �PAmeh/Ameh � Cmeh � Vmeh/Wt � 100% (5)
This journal is © The Royal Society of Chemistry 2020
Fig. 2 The scatter plots of the forecasted specific surface areasagainst the actual crystal range for the training set of data for improvedtopologies of all the chosen algorithms.
Fig. 3 The scatter plots of the forecasted specific surface areasagainst the testing data set for optimized topologies of all the selectedalgorithms.
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Fig. 4 The scatter plots of the predicted surface areas versus actualspecific surface areas for validation set of data for the selected LM-4-7-1 model.
Fig. 5 Schematic representation of multilayer perception neuralnetwork.
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where, C is the proportion of ester yield,P
A is the summationof the full area for the FFA peaks, Ameh is the peak area of theinternal-standard, Cmeh is the total loading of methyl heptade-canoate, Vmeh is the total volume ofmethyl heptadecanoate, andWt is the mass of the produced FAME.
Results and discussionsANN modelling results
The topology of the algorithms. Fig. 1 demonstrates theRMSE versus node number of the mean specic surface area ofthe mesoporous network's hidden layer for BBP, QP, LM, IBP,and GA. The selected topologies for all the ve algorithms were4-6-1, 4-15-1, 4-13-1, 4-7-1, and 4-15-1 for QP, BBP, IBP, LM, andGA algorithms, respectively. Obviously, LM-4-7-1 was chosen asthe optimum algorithm because it has the lowest RMSE value asthe conditional version for the specic surface area of themesoporous NiO-ICG nanocrystalline material. Therefore, theselected LM-4-7-1 model was further studied to choose a supe-rior model.
Model selection. To verify the excellent model for the eval-uation of specic surface area, the RMSE, R2, and AAD valueswere determined comparatively among all the highlightedtopologies. Fig. 2 depicts the actual and predicted values ofspecic surface areas for the training data. Similarly, the R2
values for the training data were calculated, as shown in Fig. 3.It is evident that LM-4-7-1 topology had the maximum R2 valueof 0.986 for the testing set of data and 0.998 for the trainingoutputs.
Moreover, the AAD values of the training and testing outputsfor all the preferred topologies are summarized in Table 2.Evidently, LM-4-7-1 with the lowermost AAD value in the testingand training was preferred as the superior model because ofpossessing the highest R2 value and the lowest RMSE and AADvalues in comparison with others.
Validation of the model. The actual and predicted specicsurface area values for the validation set of data for the selectedLM-4-7-1 model are demonstrated in the scatter plot (see Fig. 4).As represented, the high R2 value of 0.991 and low RMSE valueof 2.405 and AAD value of 0.665 validated the ultimate analyticalaccuracy of the optimized pattern.
The network of the selected model. The schematic repre-sentation of the selected multilayer perception NN model forthe specic surface areas of the mesoporous NiO-ICG catalyst isshown in Fig. 5. The selected model possessed three layers
Table 2 Improved topologies, QP-4-6-1, IBP-4-13-1, BBP-4-15-1, GA-mesoporous NiO-ICG nanocrystalline material
Learning algorithm Architecture
Training data set
RMSE R2 A
QP 4-6-1 1.354 0.994 0IBP 4-13-1 1.286 0.994 0BBP 4-15-1 1.031 0.996 0GA 4-15-1 6.722 0.851 1LM 4-7-1 0.702 0.998 0
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containing one input layer, one hidden layer, and one outputlayer. The four nodes of the input layer corresponded as thedisseminator for the 6 nodes of the hidden layer, which wasevaluated through the learning process.
The graphical optimization. The selected network can helpto navigate the specic surface area values of the synthesizedNiO-ICG through graphical optimization of the important andeffective elements. The selected LM-4-7-1 model was introducedin order to simulate the association between the chosenelements on the SBET of the produced mesoporous NiO-ICGcomposite. In each three-dimensional (3D) plot format, the
4-15-1, and LM-4-7-1 on the specific surface area of the synthesized
Testing data set
AD p value RMSE R2 AAD p value
.325 0.001 1.640 0.974 0.397 0.001
.316 0.001 1.301 0.993 0.327 0.001
.262 0.001 1.532 0.993 0.367 0.001
.605 0.001 7.413 0.888 1.826 0.001
.153 0.001 0.801 0.986 0.203 0.001
This journal is © The Royal Society of Chemistry 2020
Fig. 6 The surface response of instantaneous impact of two variables on the specific surface area value of mesoporous NiO-ICG catalyst. (a)Correlation between PEG and ICG concentrations, (b) correlation between Ni and ICG concentrations, (c) correlation between ICG concen-tration and calcination temperature, (d) correlation between Ni and PEG concentrations, (e) correlation between calcination temperature andPEG concentration, (f) correlation between calcination temperature and Ni concentration, where the other two variables were retaineduntouched at the midpoint values in each plot.
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Table 3 The alterations between the actual and the model-predicted specific surface area value of the mesoporous NiO-ICG composite
ICG con. (g) PEG con. (g) Ni con. (mmol)Calcinationtemp. (�C)
Actual surfacearea (m2 g�1)
Predicted surfacearea (m2 g�1)
Error(%)
Optimized variable 15 12 6 600 351 317.8 9.45
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non-linear interaction between two different elements versusthe specic surface area value is graphically represented inFig. 6 whereas two more variables remained untouched at themidpoint values in each plot. In the present study, the canonicalmiddle point for Ni concentration, PEG concentration, ICGconcentration, and calcination temperature was 6 g, 12 g, 10 g,and 600 �C, respectively.
Fig. 6(a) illustrates the connections between the ICG ratioand PEG concentration. As shown, by increasing the ICGconcentration from 0 to 16, the surface area increased while byfurther increasing the concentration from 16 to 20, the surfacearea decreased while the surface area was constant onincreasing the PEG concentration.
Fig. 6(b) presents the interaction between two variables ofICG concentration and Ni concentration. As observed, thesurface area was increased by increasing the ICG concentration,while the surface area decreased by increasing the Niconcentration.
Fig. 6(c) illustrates the interactions area of calcinationtemperature and ICG concentration. As presented, the surface
Fig. 7 The percentage significance of metal ratio, surfactant andtemplate concentrations, and calcination temperature on the specificsurface area value.
Fig. 8 Graphic illustration of the development of C@Ni using autoclave
13310 | RSC Adv., 2020, 10, 13302–13315
area was increased by increasing the ICG concentration, whilethe surface area decreased by increasing the calcinationtemperature.
Fig. 6(d) shows the interactions between PEG concentrationand Ni concentration. It was observed that by increasing the Niconcentration from 0 to 4, the surface area was increased whileby further increasing the concentration from 4 to 8, the surfacearea decreased. The highest SBET of the 3D plot was obtained athigher strengths of PEG and low concentrations of Ni (at about3 mmol).
Fig. 6(e) demonstrates the 3D plot of PEG concentrationagainst the calcination temperature. As demonstrated, higherSBET was obtained using the low calcination temperature andhigh PEG concentration.
Fig. 6(f) illustrates the interactions between the Ni concen-tration and calcination temperature. By expanding the quantityof Ni, the SBET decreased, whilst by rising the calcinationtemperature, the SBET improved.
Table 3 indicates the alterations in the actual and the model-predicted SBET of the NiO-ICG over the predicted optimumcondition. Noticeably, the actual specic surface area was fairlynear to the predicted value by means of the model with 9.45%miscalculation.
Importance of effective parameters. The percentage ofsignicance of metal ratio, surfactant and template concentra-tions, and post-annealing temperature on the SBET value of theauthenticated model is illustrated in Fig. 7. Accordingly, thecalcination temperature with a virtual efficiency of 29.97%showed the most effectual inuence on the specic surface areavalue while the other parameters such as Ni and PEG concen-trations also had extensive effects. It is worthy to mention thatall the variables had their specic and typical importance.
Proposed strategy of NiO heterogeneous sphere fabrication
The mechanism designed for the formation C@Ni core–shellsolids using PEG-assisted method is displayed in Fig. 8. The
-assisted method including TEM image of the C@Ni solid-spheres.
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Fig. 9 (a) Nitrogen adsorption–desorption isotherms of the synthesizedmesoporous NiO-ICGmaterial and (b) pore size distribution determinedby BJH method.
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formation and development of C@Ni core–shell solid spheresoccurred over a sequence of the following reaction steps ina Teon-lined stainless-steel autoclave:36,37
nNi(NO3) / nNi2+ + [NO3]2n (I)
nNi2+ + OH� / NiOH� (II)
NiOH� / NiOH + e (III)
NiOH + OH� / Ni(OH)2 + e (IV)
[Ni(OH)2]n + nPEG / [Ni(OH)2–PEG]n (V)
Under autoclave reaction restrictions (high temperature highpressure) the ICG as the carbon source decomposed to the gassycomposites (H2, CO, and CO2). These compounds further func-tioned as templates to build up primary spheres. Along with theformation carbonaceous spheres, the nickel nitrate decomposedto Ni2+ cation, which electrostatically interacted with –OH(hydrophilic species) to form the structure Ni(OH)2 (steps (I)–(III)). As time went by, a complex compound of [Ni(OH)2]n formedas the neighbouring Ni(OH)2 and interacted together via hydro-philic species (steps (IV)). Next, the oxygen atoms of PEGcomponents basically adsorbed at the positive charge of Ni(OH)2
Table 4 The textural characteristics of the proposed mesoporous NiO-
Sample SBETa (m2 g�1) Dp
b (n
Optimized NiO-ICG 351.15 4.40SO3H–NiO-ICG 335.30 3.25
a Specic surface area were obtained from Brunauer, Emmett and Teller musing the BJH model. c Total pore volumes were calculated at P/Po ¼ 0.99 ofrom the values of FWHM of the (200) diffraction peak from the Scherrer
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through H2 bonding to develop chains of [Ni(OH)2–PEG]n (step(V)). Basically, PEG adsorbed on the exterior layer of the particlesas a result of water-soluble extended connection and hinderedthe adhesion and agglomeration of metal particles. At 200 �C,Ni(OH)2 transformed into NiO, which further interacted and wasincorporated into the carbon sphere surrounded by a shell of NiOnanoparticles placed at the outer surface of the carbonaceouscore walls. Next, the post-annealing process was carried out totransform the C@Ni architecture into a mesoporous structureover nitrogen ow, which ultimately led to the formation of a soshell of NiO nanoparticles around the carbonaceous core walls.As illustrated in Fig. 8, the low intensication TEM picturereveals the development of a noticeable core sphere witha diameter of about 550 nmand shell structure with a diameter ofabout 50 nm. The NiO shield and ICG nucleus were indicated ingrey and black colours, respectively.
Catalyst characterization
The N2 ads–des isotherm and pore size distribution of theproduced mesoporous SO3H–NiO-ICG are demonstrated inFig. 9. The N2 ads–des isotherm was characteristic of type IVaccording to the BDDT isotherm classication, whichconrmed the mesoporous structure of the fabricated catalyst.31
ICG and SO3H–NiO-ICG catalyst
m) Vpc (cm3 g�1) Crystallite sized (nm)
0.36 34.140.26 40.50
ethod. b Average pores size was calculated from the N2 desorption branchn the N2 adsorption isotherms. d Average crystallite size were calculatedequation.
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Fig. 10 (a) XRD pattern and (b) FT-IR spectra of the synthesized NiO-ICG composites.
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The N2 ads–desorption isotherm shows a weak and strong N2
adsorption at P/Po < 0.4 and P/Po > 0.4, respectively (seeFig. 9(a)), which conrmed the formation of mono-modalmesopore diameter distribution.
Fig. 9(b) also evidenced the mono-modal mesopore diameterdistribution with a sharp bow at 4.40 nm. The textural charac-teristics of the NiO-ICG catalyst are presented in Table 4.
The average crystal size of the synthesized NiO-ICG wasevaluated using XRD. As demonstrated in Fig. 10, the X-ray peakreections of (111), (200), (220), and (331) were detected at37.45�, 43.66�, 63.50�, and 75.75�, respectively, which wereassociated with the cubic-phase NiO nanoparticles (JCPDS: #47-1049). The degree of crystallinity of the optimized NiO-ICG wasmeasured with the Scherrer's formula:
D ¼ 0.98l/b cos qb
Fig. 11 EDX spectrum of the synthesized mesoporous NiO-ICG catalyst
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where D is the crystallite dimension (average), l is the wave-length, b is the values of full width half maximum of the peaks,and qb is the Bragg-angle. According to the calculations, NiO-ICG had a crystallinity of about 34.14 nm.
The purity of NiO-ICG was veried by EDX analysis (Fig. 11),where atomic-ratios of Ni ¼ 4.47, C¼ 50.32, and O¼ 45.21 wereidentied, which were according to the accurate stoichiometryof the elemental ratios and applied through the fabricationprocedure.
Aer verifying the optimum hydrothermal conditions, TDASwas applied to attach –SO3H species to the active sites of the as-prepared materials. According to the TPD result, the meso-porous SO3H–NiO-ICG catalyst possessed NH3 acidity of3.50 mmol g�1.
The post-sulfonation temperature got a considerable impres-sion on the textural properties where the surface area andporosity reduced to 335.30 m2 g�1 and 3.25 nm, respectively. It is
.
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Fig. 12 FESEM images of (a) the synthesized NiO-ICG composites and (b) the mesoporous SO3H–NiO-ICG catalyst.
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worthy to mention that, however, the pores size sharply droppedand the mesostructured framework was preserved, which wascorresponding to the unsuccessful bonding of sulfoniccompounds at the inner pores of the surface.
The morphology of NiO-ICG and SO3H–NiO-ICG compositeswas evaluated by FESEM image, as shown in Fig. 12(a). Theformation of carbonaceous spheres with a rough shield of NiOnanocrystalline is conrmed. Fig. 12(b) shows the morphology ofmesoporous SO3H–NiO-ICG catalyst. It proved that the core–shellsolid sphere structure slightly deformed aer post-treatment.
Fig. 13 Recyclability of SO3H–NiO-ICG catalyst for ten runs under theoptimal conditions: methanol to oil molar ratio, catalyst amount,reaction temperature, and mixing intensity of 9 : 1, 1.0 wt%, 100 �C,and 450 rpm, respectively.
Methyl ester production
Furthermore, the catalytic performance of the SO3H–NiO-ICGcatalyst was determined via transesterication of WCPO usingan autoclave under the optimized conditions: the mesoporouscatalyst amount of 1 wt%, methanol to oil molar ratio of 9 : 1,temperature of 100 �C, and mixing intensity of 450 rpm. Thehigh FAME yield of was 97.4% achieved in the presence of theproduced catalyst.
The catalytic solidity of the produced catalyst was furtherdetermined (see Fig. 13). Accordingly, the spent catalyst showedsuperior performance for ten successive reaction cycles withinconsequential loss of activity. The fatty acidmethyl ester contentdropped from 97.4% to 77.7% aer 10 successive trans-esterication reaction while the leaching of the sulfoniccompounds was negligible (2.11% to 1.53%, as measured bya CHNS elemental analyser). The superior reusability of the SO3H–
NiO-ICG catalyst was associated largely with the SBET of the catalystwhere larger pore size gives better opportunity to sulfonic func-tional species to incorporate into the mesopore frameworks.37–39
Generally speaking, ANN as an effective quantiable procedurewas highly suitable for modelling the selected operative inputvariables (such as metal ratio, surfactant and template concen-trations, and calcination temperature) to predict the mean of
This journal is © The Royal Society of Chemistry 2020
surface area values of the synthesizedmesoporous SO3H–NiO-ICGcatalyst. Furthermore, introducing ANN examination as an effi-cient numerical model would largely save the budget and time byforeseeing the outputs of the analytical procedure. In addition, itwould highly ease the analytical procedure of the critical factorsand their outcomes in complex systems.
Conclusion
Nanocrystalline mesoporous NiO-ICG core–shell solid spherecomposites were successfully fabricated by hydrothermal
RSC Adv., 2020, 10, 13302–13315 | 13313
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assisted method. The ANN method was applied to predict thespecic surface area based on the input variables includingmetal ratio, surfactant and template concentrations, and calci-nation temperature. Among the four selected effective param-eters, the calcination temperature with a relative efficiency of29.97% was found to be the most effective variable on thesurface area values. Noticeably, the actual specic surface areawas fairly near to the predicted value by means of the modelwith an error of 9.45%. Aer verifying the optimum hydro-thermal conditions, thermal decomposition of ammoniumsulphate was applied to attach –SO3H functional groups to theactive sites. The optimized NiO-ICG composite possessedunique structural and textural properties such as SBET of 351.15m2 g�1, average Dp of 4.40 nm, total Vp of 0.36 cm3 g�1, andcrystallinity of 34.14 nm. Furthermore, the catalytic activity andrecyclability of the SO3H–NiO-ICG catalyst were determined viatransesterication of WCPO. The high FAME yield of 97.4% wasattained in the presence of the SO3H–NiO-ICG catalyst while thespent catalyst showed superior catalytic activity for ten succes-sive reaction cycles with inconsequential loss of activity.
Conflicts of interest
There are no conicts to declare.
Acknowledgements
The authors would like to extend their profound gratitude to theUniversiti Putra Malaysia (UPM) for the nancial support forfunding this research work through Geran Putra UPM GP-IPB/2016/9515200. The authors acknowledge their appreciation toKing Saud University (Riyadh, Saudi Arabia) for the support ofthis research through Researchers Supporting Project number(RSP-2019/80). We also would like to convey the deepest gratitudeand appreciation to Dr Maryam Shad for her ceaseless support.
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